Name: Lucas Henrique Sousa Mello
Type: MSc dissertation
Publication date: 20/05/2016
Advisor:

Namesort descending Role
Flávio Miguel Varejão Advisor *

Examining board:

Namesort descending Role
ALEXANDRE LOUREIROS RODRIGUES Co advisor *
Alexandre Plastino de Carvalho External Examiner *
Flávio Miguel Varejão Advisor *
Thomas Walter Rauber Internal Examiner *

Summary: The objective of this work is to present the effectiveness and efficiency of algorithms for solving the loss minimization problem in Multi-Label Classification (MLC). We first prove that a specific case of loss minimization in MLC isNP-complete for the loss functions Coverage and Search Length, and therefore,no efficient algorithm for solving such problems exists unless P=NP. Furthermore, we show a novel approach for evaluating multi-label algorithms that has the advantage of not being limited to some chosen base learners, such as K-neareast Neighbor and Support Vector Machine, by simulating the distribution of labels according to multiple Beta Distributions.

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